پیوندهای مفید
اخبار و اعلانات
آمار
| تعداد نشریات | 161 |
| تعداد شماره‌ها | 6,532 |
| تعداد مقالات | 70,501 |
| تعداد مشاهده مقاله | 124,093,131 |
| تعداد دریافت فایل اصل مقاله | 97,197,469 |
برآورد رطوبت سطحی خاک و بررسی برنامهریزی آبیاری اراضی نیشکر با استفاده از مدل ذوزنقه حرارتی | ||
| تحقیقات آب و خاک ایران | ||
| دوره 53، شماره 10، دی 1401، صفحه 2209-2223 اصل مقاله (2.61 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22059/ijswr.2022.338383.669214 | ||
| نویسندگان | ||
| جمال محمدی معله زاده1؛ سعید حمزه* 2؛ عبدعلی ناصری3 | ||
| 1رئیس اداره سنجش از دور و GIS، مؤسسه تحقیقات و آموزش توسعه نیشکر، اهواز، ایران | ||
| 2دانشیار گروه سنجش از دور و GIS، دانشکده جغرافیا، دانشگاه تهران، ایران | ||
| 3استاد گروه آبیاری و زهکشی، دانشکده مهندسی علوم آب، دانشگاه شهید چمران، اهواز، ایران | ||
| چکیده | ||
| رطوبت خاک یکی از پارامترهای کلیدی در مطالعات منابع آب و برنامهریزی آبیاری میباشد. اندازهگیری رطوبت خاک در مقیاس کلان کاری بسیار زمانبر و پر هزینه است. روشهای سنتی اندازهگیری رطوبت خاک در سطح مزرعه نمیتوانند تغییرات مکانی رطوبت را به بهترین صورت نشان دهند. روشهای نوین مختلفی در خصوص استفاده از دادههای ماهوارهای جهت مدلسازی رطوبت خاک مبتنی بر تصاویر حرارتی توسعه داده شدهاند. این پژوهش در سال 1399 با هدف بررسی توانایی تصاویر ماهوارهای حرارتی در برآورد رطوبت خاک و برنامهریزی آبیاری اراضی کشت و صنعت نیشکر امیرکبیر واقع در جنوب استان خوزستان انجام شد. بههمینمنظور در فصل داشت نیشکر درصد رطوبت خاک برای 9 گذر ماهواره لندست 8 محاسبه شد و با استفاده از تعداد 180 نقطه کنترل زمینی مورد ارزیابی قرار گرفت، همچنین به منظور بررسی ارتباط رطوبت خاک با زمان آبیاری مزارع، اطلاعات روزانه آبیاری 32 مزرعه طی دوره مورد مطالعه ثبت شده بود استفاده گردید. نتایج بدست آمده نشان داد، دقت مدل در برآورد رطوبت خاک با مقادیر اندازهگیری شده در سطح مزرعه مناسب است. بهطوریکه جذر میانگین مربعات خطا نرمال شده (NRMSE) برابر 9/12 درصد و ضریب تبیین (R²) برابر 82/0 حاصل شد. همچنین نتایج رطوبت خاک در مدیریت آبیاری مزارع نیشکر نشان داد، این مدل بهدلیل استفاده از باند حرارتی به عوامل محیطی از جمله درصد رطوبت نسبی هوا، دمای هوا و آفت مؤثر است. بهطوریکه (NRMSE) با تنش رطوبتی خاک برابر 32/24%، زمان آبیاری برابر 20/22%، رطوبت متوسط برابر 7/11%، رطوبت بالا برابر 20/13% و در حال آبیاری برابر 86/8% بدست آمد. در نتیجه، دقت مدل ذوزنقه حرارتی جهت برنامهریزی آبیاری مزارع در برآورد تنش رطوبتی خاک و زمان آبیاری مزرعه در برخی از دوره‌های فصل رشد نیشکر متوسط است و برآورد مزارعی که دارای رطوبت مناسب (در حد ظرفیت زراعی)، رطوبت بالا (اشباع) و در حال آبیاری، دقت مناسب میباشد. | ||
| کلیدواژه‌ها | ||
| برنامه ریزی آبیاری؛ ذوزنقه حرارتی؛ رطوبت خاک؛ سنجش از دور؛ نیشکر | ||
| عنوان مقاله [English] | ||
| Estimating Soil Surface Moisture Content and Investigating Irrigation Schedule of Sugarcane Fields Using Thermal Trapezoidal Model | ||
| نویسندگان [English] | ||
| Jamal Mohammadi Moalezade1؛ سعید Hamzeh2؛ atefeh sayadi shahraki3 | ||
| 1Head of Remote Sensing and GIS Office, Sugarcane Development Research and Training Institute, Ahvaz, Iran | ||
| 2Associate Professor, Department of Remote Sensing and GIS, Faculty of Geography, University of Tehran, Iran | ||
| 3Professor, Department of Irrigation and Drainage, Faculty of Water Science Engineering, Shahid Chamran University, Ahvaz, Iran | ||
| چکیده [English] | ||
| Soil moisture is one of the key parameters in water resources studies and irrigation remote planning. Measuring soil moisture on a large scale is costly and very time consuming. Traditional methods of measuring soil moisture at farm level cannot show the spatial changes of moisture in the best way. Various new methods have been developed to use satellite data to model soil moisture based on thermal images. This study was conducted in 2020 with the aim of investigating the ability of thermal satellite imagery to estimate soil moisture and to plan irrigation rounds of lands in sugarcane industry of Amirkabir located in the south of Khuzestan province. For this purpose, during growing season of sugarcane, soil moisture content was calculated for 9 crossings Landsat 8 satellite and evaluated using 180 ground control points, and also daily irrigation data of 32 farms (25-hectare) were recorded during the study period. The results showed that the accuracy of the model is suitable for estimating soil moisture with the measured values at the farm level. The mean square root of normalized error (NRMSE) was 12.9% and the coefficient of determination (R²) was 0.82. Also, the results of soil moisture in irrigation management of sugarcane fields showed that thermal trapezoidal model is effective due to using thermal bands to environmental factors such as relative humidity percentage, average air temperature, pest (leaf dryness) and plant temperature, and somewhat in June and July causes errors in irrigation planning of sugarcane fields. The mean square root of normalized error (NRMSE) during soil water stress was 24.32%, during irrigation time was 22.20%, at average humidity was 11.7%, during high humidity was 13.20% and during irrigation was 8.86%. Consequently, the accuracy of thermal trapezoidal model for planning irrigation of farms in estimating soil water stress and field irrigation time in some periods of growing season is moderate and for fields having sufficient soil moisture is well. | ||
| کلیدواژه‌ها [English] | ||
| Irrigation planning, Thermal trapezoid, Soil moisture, Remote sensing, Sugarcane | ||
| مراجع | ||
|
Abayat; Muhammad, Abayat; Morteza, Abayat; Mostafa. (2022). Estimation of surface soil moisture in agricultural lands using satellite images and remote sensing indicators (case study: Shushtar city). Iran water and soil research. 53 (5) 957-970. Shah Moradi; Salah, Ghaffarian Malmiri; Hamidreza, Amini; Mohammad. (2021). Derivation of surface soil moisture index (TVDI) using temperature/vegetation scatter diagram and MODIS images. Remote Sensing and Geographical Information System in Natural Resources, 12(1), 38-62. Liu, Y., Pan, Z., Zhuang, Q., Miralles, D. G., Teuling, A. J., Zhang, T., & Niyogi, D. (2015). Agriculture intensifies soil moisture decline in Northern China. Scientific reports, 5(1), 1-9. Chen, C. F., Son, N. T., Chang, L. Y., & Chen, C. C. (2011). Monitoring of soil moisture variability in relation to rice cropping systems in the Vietnamese Mekong Delta using MODIS data. Applied Geography, 31(2), 463-475. Carlson, T. N., Gillies, R. R., & Perry, E. M. (1994). A method to make use of thermal infrared temperature and NDVI measurements to infer surface soil water content and fractional vegetation cover. Remote sensing reviews, 9(1-2), 161-173. Khazaei, M., Hamzeh, S., Weng, Q. (2020). Generating high spatial and temporal soil moisture data by disaggregation of SMAP product and its assessment in different land covers. GIScience & Remote Sensing, 1(11), 1-11. Ebrahimi, M., Alavipanah, S.K., Hamzeh, S., Amiraslani, F., Neysani Samany, N., Wigneron, J. (2018).Exploiting the synergy between SMAP and SMOS to improve brightness temperature simulations and soil moisture retrievals in arid regions. JOURNAL OF HYDROLOGY, 557(1), 740-752. Veysi, Sh., Naseri. A., Hamzeh S. (2020). Relationship Between Field Measurement of Soil Moisture in the Effective Depth of Sugarcane Root Zone and Extracted Indices from Spectral Reflectance of Optical/Thermal Bands of Multispectral Satellite Images. Journal of the Indian Society of Remote Sensing, 48(7), 1035-1044. Veysi, Sh., Naseri. A., Hamzeh S., Bartholomeus. H. (2017). A satellite based crop water stress index for irrigation scheduling in sugarcane fieldsAGRICULTURAL WATER MANAGEMENT, 189(189), 70-86. Long, D., & Singh, V. P. (2012). A two-source trapezoid model for evapotranspiration (TTME) from satellite imagery. Remote Sensing of Environment, 121, 370-388. Zhang, D., Tang, R., Tang, B. H., Wu, H., & Li, Z. L. (2014). A simple method for soil moisture determination from LST–VI feature space using nonlinear interpolation based on thermal infrared remotely sensed data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(2), 638-648. Sun, H. (2015). Two-stage trapezoid: A new interpretation of the land surface temperature and fractional vegetation coverage space. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9(1), 336-346. Burdun, I., Bechtold, M., Sagris, V., Komisarenko, V., De Lannoy, G., & Mander, Ü. (2020). A comparison of three trapezoid models using optical and thermal satellite imagery for water table depth monitoring in Estonian bogs. Remote Sensing, 12(12), 1980. Mallick, K., Bhattacharya, B. K., & Patel, N. K. (2009). Estimating volumetric surface moisture content for cropped soils using a soil wetness index based on surface temperature and NDVI. Agricultural and Forest Meteorology, 149(8), 1327-1342. Moran, M. S., Peters-Lidard, C. D., Watts, J. M., & McElroy, S. (2004). Estimating soil moisture at the watershed scale with satellite-based radar and land surface models. Canadian journal of remote sensing, 30(5), 805-826. Rahimzadeh-Bajgiran, P., Omasa, K., & Shimizu, Y. (2012). Comparative evaluation of the Vegetation Dryness Index (VDI), the Temperature Vegetation Dryness Index (TVDI) and the improved TVDI (iTVDI) for water stress detection in semi-arid regions of Iran. ISPRS Journal of Photogrammetry and Remote Sensing, 68, 1-12. Rongali, G., Keshari, A. K., Gosain, A. K., & Khosa, R. (2018). A mono-window algorithm for land surface temperature estimation from Landsat 8 thermal infrared sensor data: a case study of the Beas River Basin, India. Pertanika J Sci Technol, 26(2), 829-840. Gillies, R. R., Kustas, W. P., & Humes, K. S. (1997). A verification of the'triangle'method for obtaining surface soil water content and energy fluxes from remote measurements of the Normalized Difference Vegetation Index (NDVI) and surface e. international journal of remote sensing, 18(15), 3145-3166. Nemani, R., Pierce, L., Running, S., & Goward, S. (1993). Developing satellite-derived estimates of surface moisture status. Journal of Applied Meteorology and Climatology, 32(3), 548-557. Sadeghi, M., Babaeian, E., Tuller, M., & Jones, S. B. (2017). The optical trapezoid model: A novel approach to remote sensing of soil moisture applied to Sentinel-2 and Landsat-8 observations. Remote sensing of environment, 198, 52-68. Shafian, S., & Maas, S. J. (2015). Index of soil moisture using raw Landsat image digital count data in Texas high plains. Remote Sensing, 7(3), 2352-2372. Stisen, S., Sandholt, I., Nørgaard, A., Fensholt, R., & Jensen, K. H. (2008). Combining the triangle method with thermal inertia to estimate regional evapotranspiration—Applied to MSG-SEVIRI data in the Senegal River basin. Remote Sensing of Environment, 112(3), 1242-1255. Goward, S. N., Xue, Y., & Czajkowski, K. P. (2002). Evaluating land surface moisture conditions from the remotely sensed temperature/vegetation index measurements: An exploration with the simplified simple biosphere model. Remote sensing of environment, 79(2-3), 225-242. Carlson, T. N., Gillies, R. R., & Perry, E. M. (1994). A method to make use of thermal infrared temperature and NDVI measurements to infer surface soil water content and fractional vegetation cover. Remote sensing reviews, 9(1-2), 161-173. Carlson, T. (2007). An overview of the" triangle method" for estimating surface evapotranspiration and soil moisture from satellite imagery. Sensors, 7(8), 1612-1629. Wigneron, J. P., Olioso, A., Calvet, J. C., & Bertuzzi, P. (1999). Estimating root zone soil moisture from surface soil moisture data and soil‐vegetation‐atmosphere transfer modeling. Water Resources Research, 35(12), 3735-3745. Wang, W., Huang, D., Wang, X. G., Liu, Y. R., & Zhou, F. (2011). Estimation of soil moisture using trapezoidal relationship between remotely sensed land surface temperature and vegetation index. Hydrology and Earth System Sciences, 15(5), 1699-1712. Han, Y., Wang, Y., & Zhao, Y. (2010). Estimating soil moisture conditions of the greater Changbai Mountains by land surface temperature and NDVI. IEEE Transactions on Geoscience and Remote Sensing, 48(6), 2509-2515. Yang, Y., Su, H., Zhang, R., Tian, J., & Li, L. (2015). An enhanced two-source evapotranspiration model for land (ETEML): Algorithm and evaluation. Remote sensing of Environment, 168, 54-65. Weng, Q., Lu, D., & Schubring, J. (2004). Estimation of land surface temperature–vegetation abundance relationship for urban heat island studies. Remote sensing of Environment, 89(4), 467-483. | ||
|
آمار تعداد مشاهده مقاله: 367 تعداد دریافت فایل اصل مقاله: 330 |
||